Towards AI

SEPTEMBER 5, 2024

Featured Community post from the Discord Aman_kumawat_41063 has created a GitHub repository for applying some basic ML algorithms. It offers pure NumPy implementations of fundamental machine learning algorithms for classification, clustering, preprocessing, and regression. This repo is designed for educational exploration.

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Algorithm ML ML Machine Learning 95

IBM Journey to AI blog

DECEMBER 20, 2023

Each type and sub-type of ML algorithm has unique benefits and capabilities that teams can leverage for different tasks. Instead of using explicit instructions for performance optimization, ML models rely on algorithms and statistical models that deploy tasks based on data patterns and inferences. What is machine learning?

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Dataconomy

SEPTEMBER 22, 2023

Artificial intelligence (AI) can be used to automate and optimize the data archiving process. This process can help organizations identify which data should be archived and how it should be categorized, making it easier to search, retrieve, and manage the data. There are several ways to use AI for data archiving.

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Pickl AI

AUGUST 3, 2023

Introduction to Deep Learning Algorithms: Deep learning algorithms are a subset of machine learning techniques that are designed to automatically learn and represent data in multiple layers of abstraction. This process is known as training, and it relies on large amounts of labeled data. How Deep Learning Algorithms Work?

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Data Science Dojo

FEBRUARY 3, 2023

In this blog post, we’ll explore five project ideas that can help you build expertise in computer vision, natural language processing (NLP), sales forecasting, cancer detection, and predictive maintenance using Python. One project idea in this area could be to build a facial recognition system using Python and OpenCV.

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Data Science Dojo

AUGUST 19, 2024

They dive deep into artificial neural networks, algorithms, and data structures, creating groundbreaking solutions for complex issues. These professionals venture into new frontiers like machine learning, natural language processing, and computer vision, continually pushing the limits of AI’s potential.

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Data Science Dojo

AUGUST 3, 2023

It is fast, scalable, and supports a variety of machine learning algorithms. Faiss is a library for efficient similarity search and clustering of dense vectors. They are used in a variety of AI applications, such as image search, natural language processing, and recommender systems.

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Pickl AI

JULY 17, 2023

Machine Learning is a subset of artificial intelligence (AI) that focuses on developing models and algorithms that train the machine to think and work like a human. The following blog will focus on Unsupervised Machine Learning Models focusing on the algorithms and types with examples. What is Unsupervised Machine Learning?

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AWS Machine Learning Blog

SEPTEMBER 26, 2024

During the iterative research and development phase, data scientists and researchers need to run multiple experiments with different versions of algorithms and scale to larger models. However, building large distributed training clusters is a complex and time-intensive process that requires in-depth expertise.

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Dataconomy

JUNE 29, 2023

Python machine learning packages have emerged as the go-to choice for implementing and working with machine learning algorithms. The field of machine learning, known for its algorithmic complexity, has undergone a significant transformation in recent years. Why do you need Python machine learning packages?

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Pickl AI

JULY 12, 2024

Their interactive nature makes them suitable for experimenting with AI algorithms and analysing data. Machine Learning algorithms are trained on large amounts of data, and they can then use that data to make predictions or decisions about new data.

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Mlearning.ai

APRIL 8, 2023

Charting the evolution of SOTA (State-of-the-art) techniques in NLP (Natural Language Processing) over the years, highlighting the key algorithms, influential figures, and groundbreaking papers that have shaped the field. Evolution of NLP Models To understand the full impact of the above evolutionary process.

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Hacker News

AUGUST 5, 2024

When Meta introduced distributed GPU-based training , we decided to construct specialized data center networks tailored for these GPU clusters. We have successfully expanded our RoCE networks, evolving from prototypes to the deployment of numerous clusters, each accommodating thousands of GPUs.

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Data Science Dojo

MAY 10, 2024

Hence, acting as a translator it converts human language into a machine-readable form. These embeddings when particularly used for natural language processing (NLP) tasks are also referred to as LLM embeddings. Their impact on ML tasks has made them a cornerstone of AI advancements.

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Data Science Dojo

JANUARY 30, 2024

Exploring Disease Mechanisms : Vector databases facilitate the identification of patient clusters that share similar disease progression patterns. In vec t o r d a ta b a s e s , this process of querying is more optimized and efficient with the use of a sim i l a r i ty metric for searching the most sim i l a r vec t o r to our query.

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Data Science Dojo

JULY 17, 2023

It is used for machine learning, natural language processing, and computer vision tasks. TensorFlow First on the AI tool list, we have TensorFlow which is an open-source software library for numerical computation using data flow graphs. It is a cloud-based platform, so it can be accessed from anywhere.

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Smart Data Collective

JUNE 4, 2021

Data scientists use algorithms for creating data models. Whereas in machine learning, the algorithm understands the data and creates the logic. Learning the various categories of machine learning, associated algorithms, and their performance parameters is the first step of machine learning. Clustering (Unsupervised).

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Dataconomy

MARCH 13, 2023

With advances in machine learning, deep learning, and natural language processing, the possibilities of what we can create with AI are limitless. However, the process of creating AI can seem daunting to those who are unfamiliar with the technicalities involved. Train and evaluate the AI models for accuracy and efficiency.

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IBM Journey to AI blog

MARCH 21, 2024

GPUs (graphics processing units) and TPUs (tensor processing units) are specifically designed to handle complex mathematical computations central to AI algorithms, offering significant speedups compared with traditional CPUs.

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Pickl AI

NOVEMBER 28, 2024

Summary: The blog discusses essential skills for Machine Learning Engineer, emphasising the importance of programming, mathematics, and algorithm knowledge. Key programming languages include Python and R, while mathematical concepts like linear algebra and calculus are crucial for model optimisation.

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Data Science Dojo

MAY 10, 2024

Hence, acting as a translator it converts human language into a machine-readable form. These embeddings when particularly used for natural language processing (NLP) tasks are also referred to as LLM embeddings. Their impact on ML tasks has made them a cornerstone of AI advancements.

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Towards AI

OCTOBER 31, 2024

Algorithms can automatically clean and preprocess data using techniques like outlier and anomaly detection. GenAI can help by automatically clustering similar data points and inferring labels from unlabeled data, obtaining valuable insights from previously unusable sources.

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Pickl AI

AUGUST 1, 2023

Machine Learning models play a crucial role in this process, serving as the backbone for various applications, from image recognition to natural language processing. K-Means Clustering: Used to partition data into ‘k’ clusters based on similarity. regression, classification, clustering).

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Pickl AI

NOVEMBER 18, 2024

Key steps involve problem definition, data preparation, and algorithm selection. It involves algorithms that identify and use data patterns to make predictions or decisions based on new, unseen data. Types of Machine Learning Machine Learning algorithms can be categorised based on how they learn and the data type they use.

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Heartbeat

AUGUST 23, 2023

But what if there was a technique to quickly and accurately solve this language puzzle? Enter Natural Language Processing (NLP) and its transformational power. But what if there was a way to unravel this language puzzle swiftly and accurately? Algorithms can automatically detect and extract key items.

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Data Science Dojo

JUNE 20, 2023

What are large language models? A large language model, referred to as an LLM, is an advanced machine learning algorithm capable of identifying, condensing, translating, predicting, and generating various forms of text and content using extensive datasets.

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Pickl AI

DECEMBER 2, 2024

Processing frameworks like Hadoop enable efficient data analysis across clusters. For example, financial institutions utilise high-frequency trading algorithms that analyse market data in milliseconds to make investment decisions. Frequently Asked Questions What is the Role of Data Processing Frameworks in Big Data?

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AWS Machine Learning Blog

JANUARY 30, 2023

Amazon SageMaker provides a suite of built-in algorithms , pre-trained models , and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. You can use these algorithms and models for both supervised and unsupervised learning.

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Pickl AI

OCTOBER 24, 2024

Key Takeaways Scope and Purpose : Artificial Intelligence encompasses a broad range of technologies to mimic human intelligence, while Machine Learning focuses explicitly on algorithms that enable systems to learn from data. Supervised Learning : This is the most common form of ML, where algorithms learn from labelled data.

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Pickl AI

SEPTEMBER 18, 2024

Summary: Machine Learning Engineer design algorithms and models to enable systems to learn from data. A Machine Learning Engineer plays a crucial role in this landscape, designing and implementing algorithms that drive innovation and efficiency. In finance, they build models for risk assessment or algorithmic trading.

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Mlearning.ai

JANUARY 27, 2023

The technique is commonly used in natural language processing, computer vision, and information retrieval. To do this, we can use the Word2Vec function of gensim, which is a popular Python library for natural language processing.

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Dataconomy

MAY 25, 2023

By leveraging advanced algorithms and machine learning techniques, IoT devices can analyze and interpret data in real-time, enabling them to make informed decisions and take autonomous actions. AI algorithms can uncover hidden correlations within IoT data, enabling predictive analytics and proactive actions.

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Towards AI

FEBRUARY 20, 2024

Beginner’s Guide to ML-001: Introducing the Wonderful World of Machine Learning: An Introduction Everyone is using mobile or web applications which are based on one or other machine learning algorithms. You might be using machine learning algorithms from everything you see on OTT or everything you shop online.

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Pickl AI

NOVEMBER 25, 2024

Processing frameworks like Hadoop enable efficient data analysis across clusters. For example, financial institutions utilise high-frequency trading algorithms that analyse market data in milliseconds to make investment decisions. Frequently Asked Questions What is the Role of Data Processing Frameworks in Big Data?

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Big Data Big Data Data Lakes Apache Hadoop 52

Mlearning.ai

MARCH 6, 2023

A Beginner’s Guide to FAISS, use-cases, Mathematical foundations & implementation FAISS (Facebook AI Similarity Search) is an open-source library developed by Facebook AI Research (FAIR) for high-dimensional data similarity search and clustering.

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Clustering Machine Learning Machine Learning Natural Language Processing 52

Pickl AI

SEPTEMBER 17, 2024

Machine Learning (ML) is a subset of AI that focuses on developing algorithms and statistical models that enable systems to perform specific tasks effectively without being explicitly programmed. Clustering algorithms, such as K-Means and DBSCAN, are common examples of unsupervised learning techniques.

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Dataconomy

AUGUST 31, 2023

Natural Language Processing (NLP) : Classification can be applied to text data to categorize messages, emails, or social media posts into different categories, such as spam vs. non-spam, positive vs. negative sentiment, or topic classification. Next, you need to select a model.

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